Methods and Apparatuses for Relating Multiple Magnetic Resonance Physical Parameters to Myelin Content in the Brain

ABSTRACT

A computerized method of generating a map of myelin tissue of a brain is described. In addition sub-maps of different myelin contents can be imaged. The method uses a simulation model comprising at least two interacting tissue compartments.

TECHNICAL FIELD

The present invention relates to a method, system and computer programproduct for generating fractional maps of brain tissue retrieved by aMagnetic Resonance (MR) quantification sequence. In particular a method,system and computer program product for generating a myelin fraction mapof the brain based on multiple physical parameters can be provided usingthe invention.

BACKGROUND

Magnetic Resonance Imaging (MRI) can generate cross-sectional images inany plane (including oblique planes). Medical MRI most frequently relieson the relaxation properties of excited hydrogen nuclei (protons) inwater and fat. When the object to be imaged is placed in a powerful,uniform magnetic field the spins of the atomic nuclei with non-integerspin numbers within the tissue all align either parallel to the magneticfield or anti-parallel. The output result of an MRI scan is an MRIcontrast image or a series of MRI contrast images.

In order to understand MRI contrast, it is important to have someunderstanding of the time constants involved in relaxation processesthat establish equilibrium following RF excitation. As the excitedprotons relax and realign, they emit energy at rates which are recordedto provide information about their environment. The realignment ofproton spins with the magnetic field is termed longitudinal relaxationand the time (typically about 1 sec) required for a certain percentageof the tissue nuclei to realign is termed “Time 1” or T1. T2-weightedimaging relies upon local dephasing of spins following the applicationof the transverse energy pulse; the transverse relaxation time(typically <100 ms for tissue) is termed “Time 2” or T2. Theserelaxation times are also expressed as relaxation rates R1 (=1/T1) andR2 (=1/T2). The total signal depends on the number of protons, or protondensity PD. On the scanner console all available parameters, such asecho time TE, repetition time TR, flip angle a and the application ofpreparation pulses (and many more), are set to certain values. Eachspecific set of parameters generates a particular signal intensity inthe resulting images depending on the characteristics of the measuredtissue.

In conventional contrast imaging the absolute signal intensity observedin the images has no direct meaning; it is rather the intensitydifference, the contrast, between different tissues that lead to adiagnosis. A more quantitative approach can be applied based on themeasurement of physical parameters such as R1, R2 and PD. These valuesare independent of scanner settings and hence reflect the underlyingtissue.

A Bloch simulation model (see e.g. Levesque R, Pike G B. Characterizinghealthy and diseased white matter using quantitative magnetizationtransfer and multicomponent T2 relaxometry: A unified view via afour-pool model. Mag Reson Med 2009; 62:1487-1496) can be set up torelate tissue composition to the expected observation of MRquantification results in a direct measurement of the included tissues,which could lead to MR computer aided diagnose.

One type of brain tissue of particular interest is called Myelin. Myelinis particularly interesting since it forms the insulating sheaths aroundthe nerve axons in the brain. Degradation or damage to myelin may leadto a range of diseases such as dementia and multiple sclerosis. It canalso be an important factor to determine the extent of edema, stroke orbrain tumors. Myelin consists of thin layers of fatty tissue(semi-solids) and water.

There is a constant need to improve diagnostic and imaging methodsrelating to MRI. In particular methods and apparatuses for improvedimaging and analysis of brain tissue such as myelin is desired.

SUMMARY

It is an object of the present invention to provide a method and devicefor relating the measurement of multiple physical parameters (e.g. thecombination of T1 and T2 relaxation times, R1 and R2 relaxation ratesand proton density PD, or a subset thereof) to tissue content of thebrain.

This object and others are obtained by the method and device as set outin the appended claims.

A tissue of particular interest in MR imaging is myelin. In accordancewith embodiments of the invention methods, apparatuses and computerprograms for deriving a myelin fraction from measurement of multiplephysical parameters (e.g. the combination of T1 and T2 relaxation times,R1 and R2 relaxation rates and proton density PD, or a subset thereof)are provided. The multiple physical parameters can be derived from asequence of MR images. Also the myelin water, the myelin semi-solids,the intra- and extracellular (interstitial) water, the non-myelinsemi-solids, the free water, the total water content, the myelin waterfraction, edema, the total myelin content of the brain and the relativemyelin fraction of the brain can be obtained and provided as output.

In accordance with one embodiment a computerized method of generating amap of myelin tissue of a brain is provided. The method comprisesgenerating maps of two or more physical properties of the brain. Themethod also comprises forming a simulation model comprising at least twointeracting tissue compartments and generating physical properties usingthe simulation model as input. The method then generates a myelinfraction map by relating observed physical properties of the generatedmaps to the physical properties generated using the simulation model,and using the relationship for generating the myelin fraction map.

In accordance with one embodiment the generated physical properties aregenerated with all possible tissue compartment distributions.

In accordance with one embodiment the two or more physical propertiescomprises one or many of T1, T2 R1, R2 and PD.

In accordance with one embodiment relating observed physical propertiesof the generated maps to the physical properties generated using thesimulation model is performed using look-up table.

In accordance with one embodiment at least one myelin sub-map from themyelin fraction map is generated.

In accordance with one embodiment the myelin sub(s) comprises one ormany of a myelin water map, a myelin semi-solids map, a intra- andextracellular (interstitial) water map, a non-myelin semi-solids map, afree water map, a total water content map, a myelin water fraction map,an edema map, a total myelin content of the brain map and a relativemyelin fraction of the brain map.

In accordance with one embodiment the interacting tissue compartmentscomprises one or many of a myelin fraction compartment, a cellularfraction compartment, a free water fraction compartment, a myelin watercompartment, a myelin semi-solids compartment, a intercellular andinterstitial water, compartment and a non-myelin semi-solidscompartment.

The invention also extends to a computerized imaging system arranged toperform the methods as described herein and also to a digital storagemedium having stored thereon computer program instructions/softwaresegments that when executed by a computer causes the computer to executethe methods as described herein.

In accordance with one exemplary embodiment the computerized imagingsystem comprises for generating a map of myelin tissue of a braincomprises a first imaging circuitry arranged to generate maps of two ormore physical properties of the brain, and a simulation model comprisingat least two interacting tissue compartments. The system furthercomprises a controller arranged to generate physical properties usingthe simulation model as input, and a controller arranged to relateobserved physical properties of the generated maps to the physicalproperties generated using the simulation model. A second imagingcircuitry that can be the same as the first image circuitry is providedand arranged to generate a myelin fraction map of the brain using therelationship.

To enable imaging of myelin a controller unit/imaging circuitry forperforming the above methods can be provided in a computer. Thecontroller(s) and or imaging circuitry can be implemented using suitablehardware and or software. The hardware can comprise one or manyprocessors that can be arranged to execute software stored in a readablestorage media. The processor(s) can be implemented by a single dedicatedprocessor, by a single shared processor, or by a plurality of individualprocessors, some of which may be shared or distributed. Moreover, aprocessor or may include, without limitation, digital signal processor(DSP) hardware, ASIC hardware, read only memory (ROM), random accessmemory (RAM), and/or other storage media.

Among the advantages of the methods described herein is that an absolutevalue of myelin fraction per unit volume can be obtained, which isuseful in understanding MRI brain images. Also the value can be obtainedbased on a short MR acquisition. In addition derived from the myelinfraction also the myelin water, the myelin semi-solids, the intra- andextracellular (interstitial) water, the non-myelin semi-solids, the freewater, the total water content, the myelin water fraction, edema, thetotal myelin content of the brain and the relative myelin fraction ofthe brain can be obtained.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will now be described in more detail by way ofnon-limiting examples and with reference to the accompanying drawings,in which:

FIG. 1 is a schematic outline of an MR system

FIG. 2 is a flow chart illustrating some procedural steps performed whengenerating an MR image.

FIGS. 3-6 are plots and illustrations further illustrating a method toderive images of myelin and related quantities.

DETAILED DESCRIPTION

In FIG. 1 a general view of a setup of a MRI system 100 is depicted. Thesystem 100 comprises a

MR scanner 101. The MR scanner is operative to generate MRI data bymeans of scanning a living object. The MR scanner is further connectedto a computer 103 for processing data generated by the scanner 101. Thecomputer comprises a central processor unit coupled to a memory and anumber of input and output ports for receiving and outputting data andinformation. The computer 103 receives input commands from one orseveral input devices generally represented by an input device 105. Theinput device may be one or many of a computer mouse, a keyboard, a trackball or any other input device. The computer 103 is further connected toa screen 107 for visualizing the processed scanner data as a contrastimage. In particular the computer 103 can comprise controllerunit(s)/imaging circuitry arranged to perform methods as describedherein.

In FIG. 2, a flow chart illustrating steps performed when generating amyelin map. First, maps of two or more physical properties of the brainare generated. This can for example be performed using the steps 201-203below.

Thus, in a step 201 one or several MR sequence(s) are generatedresulting in a range of images where intensity differences are observedresulting from T1 relaxation and T2 relaxation of the tissues in theobject, in this case the brain. This can be done by changing the scannersettings such as echo time TE, repetition time TR, the flip angle or thedelay after a pre-pulse. A fit is performed in step 202, using theseimages, to calculate physical properties such as T1, T2, R1, R2 and PD(or equivalent), calculated as quantification maps of the brain in astep 203. See for example J. B. M. Warntjes, O. Dahlqvist Leinhard, J.West and P. Lundberg, Rapid Magnetic Resonance Quantification on thebrain: Optimization for Clinical Usage, Magn Reson Med 60; 320-329(2008).

A simulation model is designed in step 204 with multiple, interactingtissue compartments, as is further exemplified below in conjunction withFIG. 3. The number of tissue compartments can be any suitable numberdeemed appropriate for the simulation model. Next, in a step 205 thesimulated signal intensity of these compartments under the influence ofthe MR sequence can be calculated. Using the simulated physicalproperties such as T1, T2 and PD, values are calculated as a function oftissue compartment distributions in a step 206. In accordance with oneembodiment all possible tissue compartment distribution can besimulated. In particular the fit as used in step 202 can be used toprovide simulations of the possible tissue compartment distributions.

These simulations can be used to generate a look-up table or descriptivefunctions in step 208 to relate any tissue distribution to thecalculated physical property, such as T1, T2 and PD, observations. Thelook-up table or similar can then be used to generate a myelin fractionmap of the brain of a patient based on the calculated physicalproperties such as T1, T2 and PD in a step 209. Based on the myelinfraction map generated in step 209, the related myelin sub-maps arecalculated in step 210, such as a myelin water map, the myelinsemi-solids map, the intra- and extracellular (interstitial) water map,the non-myelin semi-solids map, the free water map, the total watercontent map, the myelin water fraction map, the edema map, the totalmyelin content of the brain and the relative myelin fraction of thebrain. The total myelin content is the sum of the myelin fractions perunit volume over the complete brain. Similarly, the relative myelinfraction of the brain can be derived from the fractions, where relativemyelin fraction is the sum of the myelin fractions per unit volume overthe complete brain divided by the volume of the brain.

Also in accordance with another embodiment, instead of performing thesimulation and saving the result in a look-up table and then using thelook up table to relate the different physical properties it is possibleto make a curve fitting using the simulation model after themeasurement. In such an embodiment the simulation would have to beperformed every time the result was needed, but there would not be aneed to use a look-up table.

In FIG. 3 an example of a simulation model for the observed R₁, R₂ andPD of the brain is shown. Each acquisition voxel volume is partitionedinto a myelin fraction (MyF), a cellular fraction (CF) and a free waterfraction (FWF). The MyF compartment comprises of myelin water (MyW) andthe myelin semi-solids (MSS). The CF compartment comprises ofintracellular and interstitial water (IIW) and the non-myelinsemi-solids (non-MSS). The free water fraction only consists of water.Each fraction has its own physical properties, expressed in R1, R2 andPD. Between MyF and the CF there is an interaction expressed asmagnetization exchange rate k_(MyF-cF) that couples the magnetizationevolution of both fractions. Likewise there is a magnetization exchangerate k_(CF-FWF) between CF and the FWF. The effective, observable R1, R2and PD of the complete volume is the result of the behaviour of thecoupled fractions. The relaxation rate values and fractionaldistributions are only indicative and serve as example values. The sumof all compartments makes up the complete image segment/imageacquisition voxel.

All of the steps described in conjunction with FIG. 2 can be implementedin a computer by, for example but not limited to, executing suitablesoftware program loaded into the computer on a digital storage media andcausing the computer to execute the above steps. The method can also beimplemented using suitable hardware comprising suitable image circuitryand controllers in combination with different models and memories, forexample in the form of look-up tables.

It is further to be noted that the simulation model in FIG. 3 comprisesthree compartments. It is however to be understood that more (or fewer)compartments with interactions between them can be used. For example inFIG. 3, the myelin fraction compartment can be further sub-divided intomyelin water MyW and myelin semi-solids. Also the cellular fractioncompartment can be further sub-divided into intercellular andinterstitial water, IIW, and non-myelin semi-solids with respectiveinteraction.

An example of a simulation model outcome is shown in FIG. 4. Thefractional distribution is set to e.g. 30% myelin fraction, 70% cellularfraction and 0% free water fraction. The magnetization behavior overtime is calculated resulting in signal intensity curves where R1, R2 andPD can be derived. In this example the result is R1=1.68 s⁻¹, R2=12.5s⁻¹ and PD=64%. Hence a measurement of (R1, R2, PD)=(1.68, 12.5, 0.64)corresponds to (MyF, CF, FWF)=(0.3, 0.7, 0.0). This can be repeated forall possible fractional distributions to predict all potentialmeasurement results.

Thus, in FIGS. 4 a and 4 b, simulation of the expected magnetization ofthree tissue fraction distributions using the rate values used inconjunction with FIG. 2 is depicted. In FIG. 4 a the longitudinalmagnetization A as a function of delay time after a 120 degrees RFsaturation pulse. In FIG. 4 b the transverse magnetization M_(x) as afunction of echo time after a 90 degrees RF excitation pulse. Shown inFIGS. 4 a and 4 b are the magnetization of 1. 100% CF, 2. 15% MyF and85% CF and 3. 30% MyF and 70% CF. The FWF was zero in all three cases.The dashed lines are the mono-exponential fits on the correspondingsignal intensity for R₁ and R₂. For R₂ the first 10 ms echo time isignored to reflect experimental conditions. This leads, in general, toan underestimation of the calculated PD since the rapid decay at shortecho times is missed experimentally. The estimated (R₁, R₂, PD) valuesof the three curves are (0.92, 10.5, 0.85), (1.26, 11.4, 0.75) and(1.68, 12.5, 0.64), respectively.

In FIG. 5 typical R1, R2 and PD maps are shown of the brain with regionsof interest (ROI). The data inside the ROIs are plotted in the graphsbelow on the R1-R2, R1-PD and R2-PD projections of the R1-R2-PD space.The outcome of the simulation model where the myelin fraction rangesbetween 0-40% is added. The typical positions of grey matter (GM) andwhite matter (WM) are indicated by the circles.

Each point in the R1-R2-PD can be assigned a certain fractiondistribution resulting in a translation of R1, R2 and PD into a specificdistribution of myelin fraction, cellular fraction and free waterfraction. In FIG. 5 a typical example of quantitative MRI data of atransversal slice of the brain of a healthy subject (female, 38 years)at a field strength of 1.5 T is depicted: a. R₁ relaxation rate on ascale 0-3 s⁻¹, b. R₂ relaxation rate on a scale 5-20 s⁻¹, c. Protondensity on a scale 50-100% where 100% corresponds to pure water at 37°C. Three regions of interest are indicated where the R₁-R₂-PD data isplotted on the R₁-R₂, R₁-PD and R₂-PD projection planes below theimages. The MyF/CF ratio is changed between 0/100% to 40/60% in steps of4%, as indicated with the square markers. The mean GM and WM clusterpositions are indicated with the circles. In the R₁ image the ROI isplaced in an area with partial volume of GM, WM and CSF. The resultingdata envelope has a typical bend at the GM position and data along theindicated line. In the R₂ plot the ROI is placed in an area with only WMand in the PD plot the ROI is placed at the thalamus.

In FIG. 6 a number of fractions and derived quantities are shown of thesame slice of FIG. 5 Myelin fraction (b), cellular fraction (c), myelinwater fraction (e) and total water content (f). For comparison aconventional T2W (a) and a T1W (d) image are added. T2W image (a) of theslice displayed in FIG. 4 with the calculated myelin fraction on a scale0-40% (b) and the cellular fraction, on a scale 50-100% (c). Alsodisplayed is the T1W image of the slice (d) with the MWF (MyW/totalwater content), on a scale 0-30% (e) and the total water content on theslice (corresponding to MyW+IIW, where the CSF is not masked) on a scale50-100% (f). The red intracranial cavity outline is displayed in allimages for visual guidance.

1-21. (canceled)
 22. A computerized method of generating a map of myelintissue of a brain, comprising: generating maps of two or more physicalproperties of the brain; forming a simulation model comprising at leasttwo interacting tissue compartments; generating physical propertiesusing the simulation model as input; relating observed physicalproperties of the maps to physical properties generated using thesimulation model; and generating a myelin fraction map of the brainbased on related observed physical properties of the maps to physicalproperties generated using the simulation model.
 23. The method of claim22, wherein physical properties generated using the simulation model asinput are generated with all possible tissue compartment distributions.24. The method of claim 22, wherein the two or more physical propertiesinclude at least one of longitudinal relaxation time T1, transverserelaxation time T2, longitudinal relaxation rate R1, transverserelaxation rate R2, and proton density PD.
 25. The method of claim 22,wherein relating observed physical properties of the maps to physicalproperties generated using the simulation model includes using a look-uptable.
 26. The method of claim 22, further comprising generating atleast one myelin sub-map from the myelin fraction map.
 27. The method ofclaim 26, wherein the at least one myelin sub-map comprises at least oneof a myelin water map, a myelin semi-solids map, an intra- andextra-cellular water map, a non-myelin semi-solids map, a free watermap, a total water content map, a myelin water fraction map, an edemamap, a total myelin content of the brain map, and a relative myelinfraction of the brain map.
 28. The method of claim 22, wherein the atleast two interacting tissue compartments include at least one of amyelin fraction compartment, a cellular fraction compartment, a freewater fraction compartment, a myelin water compartment, a myelinsemi-solids compartment, an intercellular and interstitial watercompartment, and a non-myelin semi-solids compartment.
 29. Acomputerized imaging system for generating a map of myelin tissue of abrain, comprising: a first imaging circuitry configured to generate mapsof at least two physical properties of the brain; a simulation model,comprising at least two interacting tissue compartments; a firstcontroller configured to generate physical properties using thesimulation model as input; a second controller configured to relateobserved physical properties of generated maps to physical propertiesgenerated using the simulation model; and a second imaging circuitryarranged to generate a myelin fraction map of the brain using relatedobserved physical properties and physical properties generated using thesimulation model.
 30. The system of claim 29, wherein the firstcontroller is configured to generate physical properties with allpossible tissue compartment distributions.
 31. The system of claim 29,wherein the at least two physical properties include at least one oflongitudinal relaxation time T1, transverse relaxation time T2,longitudinal relaxation rate R1, transverse relaxation rate R2, andproton density PD.
 32. The system of claim 29, further comprising alook-up table configured to relate observed physical properties ofgenerated maps to physical properties generated using the simulationmodel.
 33. The system of claim 29, wherein the system further comprisesimaging circuitry configured to generate at least one myelin sub-mapfrom the myelin fraction map.
 34. The system of claim 33, wherein the atleast one myelin sub-map comprises at least one of a myelin water map, amyelin semi-solids map, an intra- and extracellular water map, anon-myelin semi-solids map, a free water map, a total water content map,a myelin water fraction map, an edema map, a total myelin content of thebrain map, and a relative myelin fraction of the brain map.
 35. Thesystem of claim 29, wherein the at least two interacting tissuecompartments include at least one of a myelin fraction compartment, acellular fraction compartment, a free water fraction compartment, amyelin water compartment, a myelin semi-solids compartment, anintercellular and interstitial water compartment, and a non-myelinsemi-solids compartment.
 36. A non-transitory digital storage mediumhaving computer program instructions stored thereon, the computerprogram instructions when executed by a computer causing the computer toperform: generating maps of at least two physical properties of a brain;forming a simulation model comprising at least two interacting tissuecompartments; generating physical properties using the simulation modelas input; relating observed physical properties of generated maps tophysical properties generated using the simulation model; and generatinga myelin fraction map of the brain based on related observed physicalproperties and physical properties generated using the simulation model.37. The non-transitory storage medium of claim 36, wherein generatedphysical properties are generated with all possible tissue compartmentdistributions.
 38. The non-transitory storage medium of claim 36,wherein the at least two physical properties include at least one oflongitudinal relaxation time T1, transverse relaxation time T2,longitudinal relaxation rate R1, transverse relaxation rate R2, andproton density PD.
 39. The non-transitory storage medium of claim 36,wherein relating observed physical properties of generated maps tophysical properties generated using the simulation model includes usinga look-up table.
 40. The non-transitory storage medium of claim 36,wherein the computer is further caused to perform generating at leastone myelin sub-map from the myelin fraction map.
 41. The non-transitorystorage medium of claim 40, wherein the at least one myelin sub-mapcomprises at least one of a myelin water map, a myelin semi-solids map,an intra- and extracellular water map, a non-myelin semi-solids map, afree water map, a total water content map, a myelin water fraction map,an edema map, a total myelin content of the brain map, and a relativemyelin fraction of the brain map.
 42. The non-transitory storage mediumof claim 36, wherein the at least two interacting tissue compartmentsinclude at least one of a myelin fraction compartment, a cellularfraction compartment, a free water fraction compartment, a myelin watercompartment, a myelin semisolids compartment, an intercellular andinterstitial water compartment, and a non-myelin semisolids compartment.